Feedback controller for data transmissions

ABSTRACT

A feedback control system for data transmissions in voice activated data packet based computer network environment is provided. A system can receive audio signals detected by a microphone of a device. The system can parse the audio signal to identify trigger keyword and request. The system can select a content item using the trigger keyword or request. The content item can be configured to establish a communication session between the device and a third party device. The system can monitor the communication session to measure a characteristic of the communication session. The system can generate a quality signal based on the measured characteristic.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 120 asa continuation of U.S. patent application Ser. No. 16/544,367, filedAug. 19, 2019, which claims the benefit of priority under 35 U.S.C. §120 as a continuation of U.S. patent application Ser. No. 15/395,694,filed Dec. 30, 2016, each of which is hereby incorporated by referenceherein in its entirety.

BACKGROUND

Excessive network transmissions, packet-based or otherwise, of networktraffic data between computing devices can prevent a computing devicefrom properly processing the network traffic data, completing anoperation related to the network traffic data, or timely responding tothe network traffic data. The excessive network transmissions of networktraffic data can also complicate data routing or degrade the quality ofthe response if the responding computing device is at or above itsprocessing capacity, which may result in inefficient bandwidthutilization. The control of network transmissions corresponding tocontent item objects can be complicated by the large number of contentitem objects that can initiate network transmissions of network trafficdata between computing devices.

SUMMARY

The present disclosure is generally directed to a feedback controllerfor data transmissions over one or more interfaces or one or more typesof computer networks. For example, computing systems may have access toa limited number of interfaces, limited types of interfaces, or theremay a limited number of available interfaces at a given time. It can bechallenging for a system to efficiently transmit information in responseto the currently available interfaces because certain types ofinterfaces may consume greater computing resources or battery. It can bechallenging to efficiently, reliably, and accurately communicateinformation over disparate computing resources because it is challengingfor disparate computing resource to efficiently process, andconsistently and accurately parse audio-based instructions in avoice-based computing environment. For example, the disparate computingresources may not have access to the same voice models, or may haveaccess to out of date or unsynchronized voice models that can make itchallenging to accurately and consistently parse the audio-basedinstructions.

Systems and methods of the present disclosure are generally directed toa feedback controller for data transmissions. The data processing systemcan process the voice-based input using voice models that are trainedbased on aggregate voice to parse the voice-based instructions andselect content items via a real-time content selection process performedby a content selector component. The data processing system can transmitthe selected content item to the client computing device to initiate acommunication session between the client computing device and a thirdparty provider device associated with the selected content item. Thedata processing system can monitor or otherwise receive informationabout the communication session to measure a characteristic of thecommunication session and generate a quality signal. The data processingsystem can then adjust or control the content selector component basedon the quality signal in order to affect the real-time content selectionprocess. For example, blocking or preventing the content selectorcomponent from selecting content item objects associated with lowquality communication sessions can reduce wasted resource consumption ascompared to allowing or permitting the content item to be selected andestablish a communication session. Further, for client devices that areutilize battery power, the feedback monitor component can save batteryusage.

At least one aspect is directed to a feedback control system for datatransmissions over a computer network. The system can include a dataprocessing system that executes a natural language processor and acontent selector component. The system can include a feedback monitorcomponent. The natural language processor component can receive, via aninterface of the data processing system, data packets comprising aninput audio signal detected by a sensor of a client device. The naturallanguage processor component can parse the input audio signal toidentify a request and a trigger keyword corresponding to the request.The data processing system can include a content selector component toreceive the trigger keyword identified by the natural language processorand to select, based on the trigger keyword, a content item via areal-time content selection process. The system can include a feedbackmonitor component. The feedback monitor component can receive datapackets carrying auditory signals transmitted between the client deviceand a conversational application programming interface that establisheda communication session with the client device responsive to interactionwith the content item. The feedback monitor can measure a characteristicof the communication session based on the auditory signals. The feedbackmonitor component can generate a quality signal based on the measuredcharacteristic. The content selector component can adjust the real-timeselection process based on the quality signal.

At least one aspect is directed to a method of transmitting data over acomputer network using a feedback control system. The method can beperformed, at least in part, by a data processing system executing anatural language processor component and content selector component. Themethod can be performed at least in part by a feedback monitorcomponent. The method can include the natural language processorcomponent receiving, via an interface of the data processing system,data packets comprising an input audio signal detected by a sensor of aclient device. The method can include the data processing system parsingthe input audio signal to identify a request and a trigger keywordcorresponding to the request. The method can include the contentselector component receiving the trigger keyword identified by thenatural language processor. The method can include the content selectorcomponent selecting, based on the trigger keyword, a content item via areal-time content selection process. The method can include the feedbackmonitor component receiving data packets carrying auditory signalstransmitted between the client device and a conversational applicationprogramming interface that established a communication session with theclient device responsive to interaction with the content item. Themethod can include the feedback monitor component measuring a quality ofthe communication session based on the auditory signals. The method caninclude the feedback monitor component generating a quality signal basedon the measured characteristic. The method can include the feedbackmonitor component adjusting the real-time selection process based on thequality signal.

These and other aspects and implementations are discussed in detailbelow. The foregoing information and the following detailed descriptioninclude illustrative examples of various aspects and implementations,and provide an overview or framework for understanding the nature andcharacter of the claimed aspects and implementations. The drawingsprovide illustration and a further understanding of the various aspectsand implementations, and are incorporated in and constitute a part ofthis specification.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. Likereference numbers and designations in the various drawings indicate likeelements. For purposes of clarity, not every component may be labeled inevery drawing. In the drawings:

FIG. 1 is an illustration of a feedback control system for datatransmissions over a computer network.

FIG. 2 is an illustration of an operation of a feedback control systemfor data transmissions over a computer network.

FIG. 3 is an illustration of a method of transmitting data over acomputer network using feedback control system.

FIG. 4 is a block diagram illustrating a general architecture for acomputer system that can be employed to implement elements of thesystems and methods described and illustrated herein.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systems ofa feedback control systems for data transmissions over a computernetwork. The various concepts introduced above and discussed in greaterdetail below may be implemented in any of numerous ways.

The present disclosure is generally directed to a feedback controllerfor data transmissions over one or more interfaces or one or more typesof computer networks. For example, computing systems may have access toa limited number of interfaces, limited types of interfaces, or theremay a limited number of available interfaces at a given time. It can bechallenging for a system to efficiently transmit information in responseto the currently available interfaces because certain types ofinterfaces may consume greater computing resources or battery. It can bechallenging to efficiently, reliably, and accurately communicateinformation over disparate computing resources because it is challengingfor disparate computing resource to efficiently process, andconsistently and accurately parse audio-based instructions in avoice-based computing environment. For example, the disparate computingresources may not have access to the same voice models, or may haveaccess to out of date or unsynchronized voice models that can make itchallenging to accurately and consistently parse the audio-basedinstructions.

Systems and methods of the present disclosure are generally directed toa feedback controller for data transmissions. The data processing systemcan process the voice-based input using voice models that are trainedbased on aggregate voice to parse the voice-based instructions andselect content items via a real-time content selection process performedby a content selector component. The data processing system can transmitthe selected content item to the client computing device to initiate acommunication session between the client computing device and a thirdparty provider device associated with the selected content item. Thedata processing system can monitor or otherwise receive informationabout the communication session to measure a characteristic of thecommunication session and generate a quality signal. The data processingsystem can then adjust or control the content selector component basedon the quality signal in order to affect the real-time content selectionprocess.

FIG. 1 illustrates an example feedback control system 100 for datatransmissions over a computer network. The system 100 can includecontent selection infrastructure. The system 100 can include a dataprocessing system 102. The data processing system 102 can communicatewith one or more of a content provider computing device 106, serviceprovider computing device 108, or client computing device 104 via anetwork 105. The network 105 can include computer networks such as theInternet, local, wide, metro, or other area networks, intranets,satellite networks, and other communication networks such as voice ordata mobile telephone networks. The network 105 can be used to accessinformation resources such as web pages, web sites, domain names, oruniform resource locators that can be presented, output, rendered, ordisplayed on at least one computing device 104, such as a laptop,desktop, tablet, personal digital assistant, smart phone, portablecomputers, or speaker. For example, via the network 105 a user of thecomputing device 104 can access information or data provided by aservice provider 108 or content provider 106.

The network 105 can include or constitute a display network, e.g., asubset of information resources available on the internet that areassociated with a content placement or search engine results system, orthat are eligible to include third party content items as part of acontent item placement campaign. The network 105 can be used by the dataprocessing system 102 to access information resources such as web pages,web sites, domain names, or uniform resource locators that can bepresented, output, rendered, or displayed by the client computing device104. For example, via the network 105 a user of the client computingdevice 104 can access information or data provided by the contentprovider computing device 106 or the service provider computing device108.

The network 105 may be any type or form of network and may include anyof the following: a point-to-point network, a broadcast network, a widearea network, a local area network, a telecommunications network, a datacommunication network, a computer network, an ATM (Asynchronous TransferMode) network, a SONET (Synchronous Optical Network) network, a SDH(Synchronous Digital Hierarchy) network, a wireless network and awireline network. The network 105 may include a wireless link, such asan infrared channel or satellite band. The topology of the network 105may include a bus, star, or ring network topology. The network mayinclude mobile telephone networks using any protocol or protocols usedto communicate among mobile devices, including advanced mobile phoneprotocol (“AMPS”), time division multiple access (“TDMA”), code-divisionmultiple access (“CDMA”), global system for mobile communication(“GSM”), general packet radio services (“GPRS”) or universal mobiletelecommunications system (“UMTS”). Different types of data may betransmitted via different protocols, or the same types of data may betransmitted via different protocols.

The system 100 can include at least one data processing system 102. Thedata processing system 102 can include at least one logic device such asa computing device having a processor to communicate via the network105, for example with the computing device 104, the content providerdevice 106 (content provider 106), or the service provider device 108(or service provider 108). The data processing system 102 can include atleast one computation resource, server, processor or memory. Forexample, the data processing system 102 can include a plurality ofcomputation resources or servers located in at least one data center.The data processing system 102 can include multiple, logically-groupedservers and facilitate distributed computing techniques. The logicalgroup of servers may be referred to as a data center, server farm or amachine farm. The servers can also be geographically dispersed. A datacenter or machine farm may be administered as a single entity, or themachine farm can include a plurality of machine farms. The serverswithin each machine farm can be heterogeneous—one or more of the serversor machines can operate according to one or more type of operatingsystem platform.

Servers in the machine farm can be stored in high-density rack systems,along with associated storage systems, and located in an enterprise datacenter. For example, consolidating the servers in this way may improvesystem manageability, data security, the physical security of thesystem, and system performance by locating servers and high performancestorage systems on localized high performance networks. Centralizationof all or some of the data processing system 102 components, includingservers and storage systems, and coupling them with advanced systemmanagement tools allows more efficient use of server resources, whichsaves power and processing requirements and reduces bandwidth usage.

The system 100 can include, access, or otherwise interact with at leastone service provider device 108. The service provider device 108 caninclude at least one logic device such as a computing device having aprocessor to communicate via the network 105, for example with thecomputing device 104, the data processing system 102, or the contentprovider 106. The service provider device 108 can include at least onecomputation resource, server, processor or memory. For example, serviceprovider device 108 can include a plurality of computation resources orservers located in at least one data center. The service provider device108 can include one or more component or functionality of the dataprocessing system 102.

The content provider computing device 106 can provide audio basedcontent items for display by the client computing device 104 as an audiooutput content item. The content item can include an offer for a good orservice, such as a voice based message that states: “Would you like meto order you a taxi?” For example, the content provider computing device155 can include memory to store a series of audio content items that canbe provided in response to a voice based query. The content providercomputing device 106 can also provide audio based content items (orother content items) to the data processing system 102 where they can bestored in the data repository 124. The data processing system 102 canselect the audio content items and provide (or instruct the contentprovider computing device 104 to provide) the audio content items to theclient computing device 104. The audio based content items can beexclusively audio or can be combined with text, image, or video data.

The service provider device 108 can include, interface, or otherwisecommunicate with at least one service provider natural languageprocessor component 142 and a service provider interface 144. Theservice provider computing device 108 can include at least one serviceprovider natural language processor (NLP) component 142 and at least oneservice provider interface 144. The service provider NLP component 142(or other components such as a direct action API of the service providercomputing device 108) can engage with the client computing device 104(via the data processing system 102 or bypassing the data processingsystem 102) to create a back-and-forth real-time voice or audio basedconversation (e.g., a session) between the client computing device 104and the service provider computing device 108. The service provider NLP142 can include one or more function or feature as the NLP component 112of the data processing system 102. For example, the service providerinterface 144 can receive or provide data messages to the direct actionAPI 116 of the data processing system 102. The service providercomputing device 108 and the content provider computing device 106 canbe associated with the same entity. For example, the content providercomputing device 106 can create, store, or make available content itemsfor a car sharing service, and the service provider computing device 108can establish a session with the client computing device 106 to arrangefor a delivery of a taxi or car of the car share service to pick up theend user of the client computing device 104. The data processing system102, via the direct action API 116, the NLP component 112 or othercomponents can also establish the session with the client computingdevice, including or bypassing the service provider computing device104, to arrange for example for a delivery of a taxi or car of the carshare service.

The computing device 104 can include, interface, or otherwisecommunicate with at least one sensor 134, transducer 136, audio driver138, or pre-processor 140. The sensor 134 can include, for example, anambient light sensor, proximity sensor, temperature sensor,accelerometer, gyroscope, motion detector, GPS sensor, location sensor,microphone, or touch sensor. The transducer 136 can include a speaker ora microphone. The audio driver 138 can provide a software interface tothe hardware transducer 136. The audio driver can execute the audio fileor other instructions provided by the data processing system 102 tocontrol the transducer 136 to generate a corresponding acoustic wave orsound wave. The pre-processor 140 can be configured to detect a keywordand perform an action based on the keyword. The pre-processor 140 canfilter out one or more terms or modify the terms prior to transmittingthe terms to the data processing system 102 for further processing. Thepre-processor 140 can convert the analog audio signals detected by themicrophone into a digital audio signal, and transmit one or more datapackets carrying the digital audio signal to the data processing system102 via the network 105. In some cases, the pre-processor 140 cantransmit data packets carrying some or all of the input audio signalresponsive to detecting an instruction to perform such transmission. Theinstruction can include, for example, a trigger keyword or other keywordor approval to transmit data packets comprising the input audio signalto the data processing system 102.

The client computing device 104 can be associated with an end user thatenters voice queries as audio input into the client computing device 104(via the sensor 134) and receives audio output in the form of a computergenerated voice that can be provided from the data processing system 102(or the content provider computing device 106 or the service providercomputing device 108) to the client computing device 104, output fromthe transducer 136 (e.g., a speaker). The computer generated voice caninclude recordings from a real person or computer generated language.

The data repository 124 can include one or more local or distributeddatabases, and can include a database management system. The datarepository 124 can include computer data storage or memory and can storeone or more parameters 126, one or more policies 128, content data 130,or templates 132 among other data. The parameters 126, policies 128, andtemplates 132 can include information such as rules about a voice basedsession between the client computing device 104 and the data processingsystem 102 (or the service provider computing device 108). The contentdata 130 can include content items for audio output or associatedmetadata, as well as input audio messages that can be part of one ormore communication sessions with the client computing device 104.

The data processing system 102 can include a content placement systemhaving at least one computation resource or server. The data processingsystem 102 can include, interface, or otherwise communicate with atleast one interface 110. The data processing system 102 can include,interface, or otherwise communicate with at least one natural languageprocessor component 112. The data processing system 102 can include,interface, or otherwise communicate with at least one direct actionapplication programming interface (“API”) 116. The data processingsystem 102 can include, interface, or otherwise communicate with atleast one session handler 114. The data processing system 102 caninclude, interface, or otherwise communicate with at least one contentselector component 118. The data processing system 102 can include,interface, or otherwise communicate with at least one feedback monitorcomponent 120. The data processing system 102 can include, interface, orotherwise communicate with at least one audio signal generator 122. Thedata processing system 102 can include, interface, or otherwisecommunicate with at least one data repository 124. The at least one datarepository 124 can include or store, in one or more data structures ordatabases, parameters 126, policies 128, content data 130, or templates132. Parameters 126 can include, for example, thresholds, distances,time intervals, durations, scores, or weights. Content data 130 caninclude, for example, content campaign information, content groups,content selection criteria, content item objects or other informationprovided by a content provider 106 or obtained or determined by the dataprocessing system to facilitate content selection. The content data 130can include, for example, historical performance of a content campaign.

The interface 110, natural language processor component 112, sessionhandler 114, direct action API 116, content selector component 118,feedback monitor component 120, or audio signal generator component 122can each include at least one processing unit or other logic device suchas programmable logic array engine, or module configured to communicatewith the database repository or database 124. The interface 110, naturallanguage processor component 112, session handler 114, direct action API116, content selector component 118, feedback monitor component 120,audio signal generator component 122 and data repository 124 can beseparate components, a single component, or part of the data processingsystem 102. The system 100 and its components, such as a data processingsystem 102, can include hardware elements, such as one or moreprocessors, logic devices, or circuits.

The data processing system 102 can obtain anonymous computer networkactivity information associated with a plurality of computing devices104. A user of a computing device 104 can affirmatively authorize thedata processing system 102 to obtain network activity informationcorresponding to the user's computing device 104. For example, the dataprocessing system 102 can prompt the user of the computing device 104for consent to obtain one or more types of network activity information.The identity of the user of the computing device 104 can remainanonymous and the computing device 104 can be associated with a uniqueidentifier (e.g., a unique identifier for the user or the computingdevice provided by the data processing system or a user of the computingdevice). The data processing system can associate each observation witha corresponding unique identifier.

A content provider 106 can establish an electronic content campaign. Theelectronic content campaign can be stored as content data 130 in datarepository 124. An electronic content campaign can refer to one or morecontent groups that correspond to a common theme. A content campaign caninclude a hierarchical data structure that includes content groups,content item data objects, and content selection criteria. To create acontent campaign, content provider 106 can specify values for campaignlevel parameters of the content campaign. The campaign level parameterscan include, for example, a campaign name, a preferred content networkfor placing content item objects, a value of resources to be used forthe content campaign, start and end dates for the content campaign, aduration for the content campaign, a schedule for content item objectplacements, language, geographical locations, type of computing deviceson which to provide content item objects. In some cases, an impressioncan refer to when a content item object is fetched from its source(e.g., data processing system 102 or content provider 106), and iscountable. In some cases, due to the possibility of click fraud, roboticactivity can be filtered and excluded, as an impression. Thus, in somecases, an impression can refer to a measurement of responses from a Webserver to a page request from a browser, which is filtered from roboticactivity and error codes, and is recorded at a point as close aspossible to opportunity to render the content item object for display onthe computing device 104. In some cases, an impression can refer to aviewable or audible impression; e.g., the content item object is atleast partially (e.g., 20%, 30%, 30%, 40%, 50%, 60%, 70%, or more)viewable on a display device of the client computing device 104, oraudible via a speaker 136 of the computing device 104. A click orselection can refer to a user interaction with the content item object,such as a voice response to an audible impression, a mouse-click, touchinteraction, gesture, shake, audio interaction, or keyboard click. Aconversion can refer to a user taking a desired action with respect tothe content item objection; e.g., purchasing a product or service,completing a survey, visiting a physical store corresponding to thecontent item, or completing an electronic transaction.

The content provider 106 can further establish one or more contentgroups for a content campaign. A content group includes one or morecontent item objects and corresponding content selection criteria, suchas keywords, words, terms, phrases, geographic locations, type ofcomputing device, time of day, interest, topic, or vertical. Contentgroups under the same content campaign can share the same campaign levelparameters, but may have tailored specifications for particular contentgroup level parameters, such as keywords, negative keywords (e.g., thatblock placement of the content item in the presence of the negativekeyword on main content), bids for keywords, or parameters associatedwith the bid or content campaign.

To create a new content group, the content provider can provide valuesfor the content group level parameters of the content group. The contentgroup level parameters include, for example, a content group name orcontent group theme, and bids for different content placementopportunities (e.g., automatic placement or managed placement) oroutcomes (e.g., clicks, impressions, or conversions). A content groupname or content group theme can be one or more terms that the contentprovider 106 can use to capture a topic or subject matter for whichcontent item objects of the content group is to be selected for display.For example, a car dealership can create a different content group foreach brand of vehicle it carries, and may further create a differentcontent group for each model of vehicle it carries. Examples of thecontent group themes that the car dealership can use include, forexample, “Make A sports car” “Make B sports car,” “Make C sedan,” “MakeC truck,” “Make C hybrid,” or “Make D hybrid.” An example contentcampaign theme can be “hybrid” and include content groups for both “MakeC hybrid” and “Make D hybrid”, for example.

The content provider 106 can provide one or more keywords and contentitem objects to each content group. Keywords can include terms that arerelevant to the product or services of associated with or identified bythe content item objects. A keyword can include one or more terms orphrases. For example, the car dealership can include “sports car,” “V-6engine,” “four-wheel drive,” “fuel efficiency,” as keywords for acontent group or content campaign. In some cases, negative keywords canbe specified by the content provider to avoid, prevent, block, ordisable content placement on certain terms or keywords. The contentprovider can specify a type of matching, such as exact match, phrasematch, or broad match, used to select content item objects.

The content provider 106 can provide one or more keywords to be used bythe data processing system 102 to select a content item object providedby the content provider 106. The content provider 106 can identify oneor more keywords to bid on, and further provide bid amounts for variouskeywords. The content provider 106 can provide additional contentselection criteria to be used by the data processing system 102 toselect content item objects. Multiple content providers 106 can bid onthe same or different keywords, and the data processing system 102 canrun a content selection process or ad auction responsive to receiving anindication of a keyword of an electronic message.

The content provider 106 can provide one or more content item objectsfor selection by the data processing system 102. The data processingsystem 102 (e.g., via content selector component 118) can select thecontent item objects when a content placement opportunity becomesavailable that matches the resource allocation, content schedule,maximum bids, keywords, and other selection criteria specified for thecontent group. Different types of content item objects can be includedin a content group, such as a voice content item, audio content item, atext content item, an image content item, video content item, multimediacontent item, or content item link. Upon selecting a content item, thedata processing system 102 can transmit the content item object forrendering on a computing device 104 or display device of the computingdevice 104. Rendering can include displaying the content item on adisplay device, or playing the content item via a speaker of thecomputing device 104. The data processing system 102 can provideinstructions to a computing device 104 to render the content itemobject. The data processing system 102 can instruct the computing device104, or an audio driver 138 of the computing device 104, to generateaudio signals or acoustic waves.

The data processing system 102 can include an interface component 110designed, configured, constructed, or operational to receive andtransmit information using, for example, data packets. The interface 110can receive and transmit information using one or more protocols, suchas a network protocol. The interface 110 can include a hardwareinterface, software interface, wired interface, or wireless interface.The interface 110 can facilitate translating or formatting data from oneformat to another format. For example, the interface 110 can include anapplication programming interface that includes definitions forcommunicating between various components, such as software components.

The data processing system 102 can include an application, script orprogram installed at the client computing device 104, such as an app tocommunicate input audio signals to the interface 110 of the dataprocessing system 102 and to drive components of the client computingdevice to render output audio signals. The data processing system 102can receive data packets or other signal that includes or identifies anaudio input signal. For example, the data processing system 102 canexecute or run the NLP component 112 to receive or obtain the audiosignal and parse the audio signal. For example, the NLP component 112can provide for interactions between a human and a computer. The NLPcomponent 112 can be configured with techniques for understandingnatural language and allowing the data processing system 102 to derivemeaning from human or natural language input. The NLP component 112 caninclude or be configured with technique based on machine learning, suchas statistical machine learning. The NLP component 112 can utilizedecision trees, statistical models, or probabilistic models to parse theinput audio signal. The NLP component 112 can perform, for example,functions such as named entity recognition (e.g., given a stream oftext, determine which items in the text map to proper names, such aspeople or places, and what the type of each such name is, such asperson, location, or organization), natural language generation (e.g.,convert information from computer databases or semantic intents intounderstandable human language), natural language understanding (e.g.,convert text into more formal representations such as first-order logicstructures that a computer module can manipulate), machine translation(e.g., automatically translate text from one human language to another),morphological segmentation (e.g., separating words into individualmorphemes and identify the class of the morphemes, which can bechallenging based on the complexity of the morphology or structure ofthe words of the language being considered), question answering (e.g.,determining an answer to a human-language question, which can bespecific or open-ended), semantic processing (e.g., processing that canoccur after identifying a word and encoding its meaning in order torelate the identified word to other words with similar meanings).

The NLP component 112 converts the audio input signal into recognizedtext by comparing the input signal against a stored, representative setof audio waveforms (e.g., in the data repository 124) and choosing theclosest matches. The set of audio waveforms can be stored in datarepository 124 or other database accessible to the data processingsystem 102. The representative waveforms are generated across a largeset of users, and then may be augmented with speech samples from theuser. After the audio signal is converted into recognized text, the NLPcomponent 112 matches the text to words that are associated, for examplevia training across users or through manual specification, with actionsthat the data processing system 102 can serve.

The audio input signal can be detected by the sensor 134 or transducer136 (e.g., a microphone) of the client computing device 104. Via thetransducer 136, the audio driver 138, or other components the clientcomputing device 104 can provide the audio input signal to the dataprocessing system 102 (e.g., via the network 105) where it can bereceived (e.g., by the interface 110) and provided to the NLP component112 or stored in the data repository 124.

The NLP component 112 can obtain the input audio signal. From the inputaudio signal, the NLP component 112 can identify at least one request orat least one trigger keyword corresponding to the request. The requestcan indicate intent or subject matter of the input audio signal. Thetrigger keyword can indicate a type of action likely to be taken. Forexample, the NLP component 112 can parse the input audio signal toidentify at least one request to leave home for the evening to attenddinner and a movie. The trigger keyword can include at least one word,phrase, root or partial word, or derivative indicating an action to betaken. For example, the trigger keyword “go” or “to go to” from theinput audio signal can indicate a need for transport. In this example,the input audio signal (or the identified request) does not directlyexpress an intent for transport, however the trigger keyword indicatesthat transport is an ancillary action to at least one other action thatis indicated by the request.

The NLP component 112 can parse the input audio signal to identify,determine, retrieve, or otherwise obtain the request and the triggerkeyword. For instance, the NLP component 112 can apply a semanticprocessing technique to the input audio signal to identify the triggerkeyword or the request. The NLP component 112 can apply the semanticprocessing technique to the input audio signal to identify a triggerphrase that includes one or more trigger keywords, such as a firsttrigger keyword and a second trigger keyword. For example, the inputaudio signal can include the sentence “I need someone to do my laundryand my dry cleaning.” The NLP component 112 can apply a semanticprocessing technique, or other natural language processing technique, tothe data packets comprising the sentence to identify trigger phrases “domy laundry” and “do my dry cleaning”. The NLP component 112 can furtheridentify multiple trigger keywords, such as laundry, and dry cleaning.For example, the NLP component 112 can determine that the trigger phraseincludes the trigger keyword and a second trigger keyword.

The NLP component 112 can filter the input audio signal to identify thetrigger keyword. For example, the data packets carrying the input audiosignal can include “It would be great if I could get someone that couldhelp me go to the airport”, in which case the NLP component 112 canfilter out one or more terms as follows: “it”, “would”, “be”, “great”,“if”, “I”, “could”, “get”, “someone”, “that”, “could”, or “help”. Byfiltering out these terms, the NLP component 112 may more accurately andreliably identify the trigger keywords, such as “go to the airport” anddetermine that this is a request for a taxi or a ride sharing service.

In some cases, the NLP component can determine that the data packetscarrying the input audio signal includes one or more requests. Forexample, the input audio signal can include the sentence “I need someoneto do my laundry and my dry cleaning.” The NLP component 112 candetermine this is a request for a laundry service and a dry cleaningservice. The NLP component 112 can determine this is a single requestfor a service provider that can provide both laundry services and drycleaning services. The NLP component 112 can determine that this is tworequests; a first request for a service provider that performs laundryservices, and a second request for a service provider that provides drycleaning services. In some cases, the NLP component 112 can combine themultiple determined requests into a single request, and transmit thesingle request to a service provider device 108. In some cases, the NLPcomponent 112 can transmit the individual requests to respective serviceprovider devices 108, or separately transmit both requests to the sameservice provider device 108.

The data processing system 102 can include a direct action API 116designed and constructed to generate, based on the trigger keyword, anaction data structure responsive to the request. Processors of the dataprocessing system 102 can invoke the direct action API 116 to executescripts that generate a data structure to a service provider device 108to request or order a service or product, such as a car from a car shareservice. The direct action API 116 can obtain data from the datarepository 124, as well as data received with end user consent from theclient computing device 104 to determine location, time, user accounts,logistical or other information to allow the service provider device 108to perform an operation, such as reserve a car from the car shareservice. Using the direct action API 116, the data processing system 102can also communicate with the service provider device 108 to completethe conversion by in this example making the car share pick upreservation.

The direct action API 116 can execute a specified action to satisfy theend user's intention, as determined by the data processing system 102.Depending on the action specified in its inputs, the direct action API116 can execute code or a dialog script that identifies the parametersrequired to fulfill a user request. Such code can look-up additionalinformation, e.g., in the data repository 124, such as the name of ahome automation service, or it can provide audio output for rendering atthe client computing device 104 to ask the end user questions such asthe intended destination of a requested taxi. The direct action API 116can determine necessary parameters and can package the information intoan action data structure, which can then be sent to another componentsuch as the content selector component 118 or to the service providercomputing device 108 to be fulfilled.

The direct action API 116 can receive an instruction or command from theNLP component 112, or other component of the data processing system 102,to generate or construct the action data structure. The direct actionAPI 116 can determine a type of action in order to select a templatefrom the template repository 132 stored in the data repository 124.Types of actions can include, for example, services, products,reservations, or tickets. Types of actions can further include types ofservices or products. For example, types of services can include carshare service, food delivery service, laundry service, maid service,repair services, or household services. Types of products can include,for example, clothes, shoes, toys, electronics, computers, books, orjewelry. Types of reservations can include, for example, dinnerreservations or hair salon appointments. Types of tickets can include,for example, movie tickets, sports venue tickets, or flight tickets. Insome cases, the types of services, products, reservations or tickets canbe categorized based on price, location, type of shipping, availability,or other attributes.

The direct action API 116, upon identifying the type of request, canaccess the corresponding template from the template repository 132.Templates can include fields in a structured data set that can bepopulated by the direct action API 116 to further the operation that isrequested of the service provider device 108 (such as the operation ofsending a taxi to pick up an end user at a pickup location and transportthe end user to a destination location). The direct action API 116 canperform a lookup in the template repository 132 to select the templatethat matches one or more characteristic of the trigger keyword andrequest. For example, if the request corresponds to a request for a caror ride to a destination, the data processing system 102 can select acar sharing service template. The car sharing service template caninclude one or more of the following fields: device identifier, pick uplocation, destination location, number of passengers, or type ofservice. The direct action API 116 can populate the fields with values.To populate the fields with values, the direct action API 116 can ping,poll or otherwise obtain information from one or more sensors 134 of thecomputing device 104 or a user interface of the device 104. For example,the direct action API 116 can detect the source location using alocation sensor, such as a GPS sensor. The direct action API 116 canobtain further information by submitting a survey, prompt, or query tothe end of user of the computing device 104. The direct action API cansubmit the survey, prompt, or query via interface 110 of the dataprocessing system 102 and a user interface of the computing device 104(e.g., audio interface, voice-based user interface, display, or touchscreen). Thus, the direct action API 116 can select a template for theaction data structure based on the trigger keyword or the request,populate one or more fields in the template with information detected byone or more sensors 134 or obtained via a user interface, and generate,create or otherwise construct the action data structure to facilitateperformance of an operation by the service provider device 108.

The data processing system 102 can select the template based from thetemplate data structure 132 based on various factors including, forexample, one or more of the trigger keyword, request, third partyprovider device 108, type of third party provider device 108, a categorythat the third party provider device 108 falls in (e.g., taxi service,laundry service, flower service, or food delivery), location, or othersensor information.

To select the template based on the trigger keyword, the data processingsystem 102 (e.g., via direct action API 116) can perform a look-up orother query operation on the template database 132 using the triggerkeyword to identify a template data structure that maps or otherwisecorresponds to the trigger keyword. For example, each template in thetemplate database 132 can be associated with one or more triggerkeywords to indicate that the template is configured to generate anaction data structure responsive to the trigger keyword that the thirdparty provider device 108 can process to establish a communicationsession.

In some cases, the data processing system 102 can identify a third partyprovider device 108 based on the trigger keyword. To identify the thirdparty provide 108 based on the trigger keyword, the data processingsystem 102 can perform a lookup in the data repository 124 to identify athird party provider device 108 that maps to the trigger keyword. Forexample, if the trigger keyword includes “ride” or “to go to”, then thedata processing system 102 (e.g., via direct action API 116) canidentify the third party provider device 108 as corresponding to TaxiService Company A. The data processing system 102 can select thetemplate from the template database 132 using the identify third partyprovider device 108. For example, the template database 132 can includea mapping or correlation between third party provider devices 108 orentities to templates configured to generate an action data structureresponsive to the trigger keyword that the third party provider device108 can process to establish a communication session. In some cases, thetemplate can be customized for the third party provider device 108 orfor a category of third party provider devices 108. The data processingsystem 102 can generate the action data structure based on the templatefor the third party provider 108.

To construct or generate the action data structure, the data processingsystem 102 can identify one or more fields in the selected template topopulate with values. The fields can be populated with numerical values,character strings, Unicode values, Boolean logic, binary values,hexadecimal values, identifiers, location coordinates, geographic areas,timestamps, or other values. The fields or the data structure itself canbe encrypted or masked to maintain data security.

Upon determining the fields in the template, the data processing system102 can identify the values for the fields to populate the fields of thetemplate to create the action data structure. The data processing system102 can obtain, retrieve, determine or otherwise identify the values forthe fields by performing a look-up or other query operation on the datarepository 124.

In some cases, the data processing system 102 can determine that theinformation or values for the fields are absent from the data repository124. The data processing system 102 can determine that the informationor values stored in the data repository 124 are out-of-date, stale, orotherwise not suitable for the purpose of constructing the action datastructure responsive to the trigger keyword and request identified bythe NLP component 112 (e.g., the location of the client computing device104 may be the old location and not be the current location; an accountcan be expired; the destination restaurant may have moved to a newlocation; physical activity information; or mode of transportation).

If the data processing system 102 determines that it does not currentlyhave access, in memory of the data processing system 102, to the valuesor information for the field of the template, the data processing system102 can acquire the values or information. The data processing system102 can acquire or obtain the information by querying or polling one ormore available sensors of the client computing device 104, prompting theend user of the client computing device 104 for the information, oraccessing an online web-based resource using an HTTP protocol. Forexample, the data processing system 102 can determine that it does nothave the current location of the client computing device 104, which maybe a needed field of the template. The data processing system 102 canquery the client computing device 104 for the location information. Thedata processing system 102 can request the client computing device 104to provide the location information using one or more location sensors134, such as a Global Positioning System sensor, WIFI triangulation,cell tower triangulation, Bluetooth beacons, IP address, or otherlocation sensing technique.

The direct action API 116 can transmit the action data structure to athird party provider device (e.g., service provider device 108) to causethe third party provider device 108 to invoke a conversationalapplication programming interface (e.g., service provider NLP component142) and establish a communication session between the third partyprovider device 108 and the client computing device 104. Responsive toestablishing the communication session between the service providerdevice 108 and the client computing device 1004, the service providerdevice 108 can transmit data packets directly to the client computingdevice 104 via network 105. In some cases, the service provider device108 can transmit data packets to the client computing device 104 viadata processing system 102 and network 105.

In some cases, the third party provider device 108 can execute at leasta portion of the conversational API 142. For example, the third partyprovider device 108 can handle certain aspects of the communicationsession or types of queries. The third party provider device 108 mayleverage the NLP component 112 executed by the data processing system102 to facilitate processing the audio signals associated with thecommunication session and generating responses to queries. In somecases, the data processing system 102 can include the conversational API142 configured for the third party provider 108. In some cases, the dataprocessing system routes data packets between the client computingdevice and the third party provider device to establish thecommunication session. The data processing system 102 can receive, fromthe third party provider device 108, an indication that the third partyprovider device established the communication session with the clientdevice 104. The indication can include an identifier of the clientcomputing device 104, timestamp corresponding to when the communicationsession was established, or other information associated with thecommunication session, such as the action data structure associated withthe communication session. In some cases, the data processing system 102can include a session handler component 114 to manage the communicationsession and a feedback monitor component 120 to measure thecharacteristic of the communication session.

The data processing system 102 can include, execute, access, orotherwise communicate with a session handler component 114 to establisha communication session between the client device 104 and the dataprocessing system 102. The communication session can refer to one ormore data transmissions between the client device 104 and the dataprocessing system 102 that includes the input audio signal that isdetected by a sensor 134 of the client device 104, and the output signaltransmitted by the data processing system 102 to the client device 104.The data processing system 102 (e.g., via the session handler component114) can establish the communication session responsive to receiving theinput audio signal. The data processing system 102 can set a durationfor the communication session. The data processing system 102 can set atimer or a counter for the duration set for the communication session.Responsive to expiration of the timer, the data processing system 102can terminate the communication session.

The communication session can refer to a network-based communicationsession in which the client device 104 provides authenticatinginformation or credentials to establish the session. In some cases, thecommunication session refers to a topic or a context of audio signalscarried by data packets during the session. For example, a firstcommunication session can refer to audio signals transmitted between theclient device 104 and the data processing system 102 that are related to(e.g., include keywords, action data structures, or content itemobjects) a taxi service; and a second communication session can refer toaudio signals transmitted between the client device 104 and dataprocessing system 102 that are related to a laundry and dry cleaningservice. In this example, the data processing system 102 can determinethat the context of the audio signals are different (e.g., via the NLPcomponent 112), and separate the two sets of audio signals intodifferent communication sessions. The session handler 114 can terminatethe first session related to the ride service responsive to identifyingone or more audio signals related to the dry cleaning and laundryservice. Thus, the data processing system 102 can initiate or establishthe second session for the audio signals related to the dry cleaning andlaundry service responsive to detecting the context of the audiosignals.

The data processing system 102 can include, execute, or otherwisecommunicate with a content selector component 118 to receive the triggerkeyword identified by the natural language processor and select, basedon the trigger keyword, a content item via a real-time content selectionprocess. In some cases, the direct action API 116 can transmit theaction data structure to the content selector component 118 to performthe real-time content selection process and establish a communicationsession between the content provider device 106 (or a third partyprovider device 108) and the client computing device 104.

The content selection process can refer to, or include, selectingsponsored content item objects provided by third party content providers106. The content selection process can include a service in whichcontent items provided by multiple content providers are parsed,processed, weighted, or matched in order to select one or more contentitems to provide to the computing device 104. The content selectionprocess can be performed in real-time or offline. Performing the contentselection process in real-time can refer to performing the contentselection process responsive to the request for content received via theclient computing device 104. The real-time content selection process canbe performed (e.g., initiated or completed) within a time interval ofreceiving the request (e.g., 5 seconds, 10 seconds, 20 seconds, 30seconds, 1 minute, 2 minutes, 3 minutes, 5 minutes, 10 minutes, or 20minutes). The real-time content selection process can be performedduring a communication session with the client computing device 104, orwithin a time interval after the communication session is terminated.

For example, the data processing system 102 can include a contentselector component 118 designed, constructed, configured or operationalto select content item objects. To select content items for display in avoice-based environment, the data processing system 102 (e.g., via NLPcomponent 112) can parse the input audio signal to identify keywords(e.g., a trigger keyword), and use the keywords to select a matchingcontent item based on a broad match, exact match, or phrase match. Forexample, the content selector component 118 can analyze, parse, orotherwise process subject matter of candidate content items to determinewhether the subject matter of the candidate content items correspond tothe subject matter of the keywords or phrases of the input audio signaldetected by the microphone of the client computing device 104. Thecontent selector component 118 may identify, analyze, or recognizevoice, audio, terms, characters, text, symbols, or images of thecandidate content items using an image processing technique, characterrecognition technique, natural language processing technique, ordatabase lookup. The candidate content items may include metadataindicative of the subject matter of the candidate content items, inwhich case the content selector component 118 may process the metadatato determine whether the subject matter of the candidate content itemcorresponds to the input audio signal.

Content providers 106 may provide additional indicators when setting upa content campaign that includes content items. The content provider 106may provide information at the content campaign or content group levelthat the content selector component 118 may identify by performing alookup using information about the candidate content item. For example,the candidate content item may include a unique identifier, which maymap to a content group, content campaign, or content provider. Thecontent selector component 118 may determine, based on informationstored in content campaign data structure in data repository 124,information about the content provider 106.

The data processing system 102 can receive, via a computer network, arequest for content for presentation on a computing device 104. The dataprocessing system 102 can identify the request by processing an inputaudio signal detected by a microphone of the client computing device104. The request can include selection criteria of the request, such asthe device type, location, and a keyword associated with the request.The request can include the action data structure or action datastructure.

Responsive to the request, the data processing system 102 can select acontent item object from data repository 124 or a database associatedwith the content provider 106, and provide the content item forpresentation via the computing device 104 via network 105. The contentitem object can be provided by a content provider device 108 differentfrom the service provider device 108. The content item can correspond toa type of service different from a type of service of the action datastructure (e.g., taxi service versus food delivery service). Thecomputing device 104 can interact with the content item object. Thecomputing device 104 can receive an audio response to the content item.The computing device 104 can receive an indication to select a hyperlinkor other button associated with the content item object that causes orallows the computing device 104 to identify service provider 108,request a service from the service provider 108, instruct the serviceprovider 108 to perform a service, transmit information to the serviceprovider 108, or otherwise query the service provider device 108.

The data processing system 102 can include, execute, or communicate withan audio signal generator component 122 to generate an output signal.The output signal can include one or more portions. For example, theoutput signal can include a first portion and a second portion. Thefirst portion of the output signal can correspond to the action datastructure. The second portion of the output signal can correspond to thecontent item selected by the content selector component 118 during thereal-time content selection process.

The audio signal generator component 122 can generate the output signalwith a first portion having sound corresponding to the first datastructure. For example, the audio signal generator component 122 cangenerate the first portion of the output signal based on one or morevalues populated into the fields of the action data structure by thedirect action API 116. In a taxi service example, the values for thefields can include, for example, 123 Main Street for pick-up location,1234 Main Street for destination location, 2 for number of passengers,and economy for the level of service. The audio signal generatorcomponent 122 can generate the first portion of the output signal inorder to confirm that the end user of the computing device 104 wants toproceed with transmitting the request to the service provider 108. Thefirst portion can include the following output “Would you like to orderan economy car from taxi service provider A to pick two people up at 123Main Street and drop off at 1234 Main Street?”

In some cases, the first portion can include information received fromthe service provider device 108. The information received from serviceprovider device 108 can be customized or tailored for the action datastructure. For example, the data processing system 102 (e.g., via directaction API 116) can transmit the action data structure to the serviceprovider 108 before instructing the service provider 108 to perform theoperation. Instead, the data processing system 102 can instruct theservice provider device 108 to perform initial or preliminary processingon the action data structure to generate preliminary information aboutthe operation. In the example of the taxi service, the preliminaryprocessing on the action data structure can include identifyingavailable taxis that meet the level of service requirement that arelocated around the pick-up location, estimating an amount of time forthe nearest available taxi to reach the pick-up location, estimating atime of arrival at the destination, and estimating a price for the taxiservice. The estimated preliminary values may include a fixed value, anestimate that is subject to change based on various conditions, or arange of values. The service provider device 108 can return thepreliminary information to the data processing system 102 or directly tothe client computing device 104 via the network 104. The data processingsystem 102 can incorporate the preliminary results from the serviceprovider device 108 into the output signal, and transmit the outputsignal to the computing device 104. The output signal can include, forexample, “Taxi Service Company A can pick you up at 123 Main Street in10 minutes, and drop you off at 1234 Main Street by 9 AM for $10. Do youwant to order this ride?” This can form the first portion of the outputsignal.

In some cases, the data processing system 102 can form a second portionof the output signal. The second portion of the output signal caninclude a content item selected by the content selector component 118during a real-time content selection process. The first portion can bedifferent from the second portion. For example, the first portion caninclude information corresponding to the action data structure that isdirectly responsive to the data packets carrying the input audio signaldetected by the sensor 134 of the client computing device 104, whereasthe second portion can include a content item selected by a contentselector component 104 that can be tangentially relevant to the actiondata structure, or include sponsored content provided by a contentprovider device 106. For example, the end user of the computing device104 can request a taxi from Taxi Service Company A. The data processingsystem 102 can generate the first portion of the output signal toinclude information about the taxi from the Taxi Service Company A.However, the data processing system 102 can generate the second portionof the output signal to include a content item selected based on thekeywords “taxi service” and information contained in the action datastructure that the end user may be interested in. For example, thesecond portion can include a content item or information provided by adifferent taxi service company, such as Taxi Service Company B. Whilethe user may not have specifically requested Taxi Service Company B, thedata processing system 102 may nonetheless provide a content item fromTaxi Service Company B because the user may choose to perform anoperation with Taxi Service Company B.

The data processing system 102 can transmit information from the actiondata structure to the Taxi Service Company B to determine a pick-uptime, time of arrival at the destination, and a price for the ride. Thedata processing system 102 can receive this information and generate thesecond portion of the output signal as follows: “Taxi Service Company Bcan pick you up at 123 Main Street in 2 minutes, and drop you off at1234 Main Street by 8:52 AM for $15. Do you want this ride instead?” Theend user of computing device 104 can then select the ride provided byTaxi Service Company A or the ride provided by Taxi Service Company B.

Prior to providing, in the second portion of the output signal, thesponsored content item corresponding to the service provided by TaxiService Company B, the data processing system 102 can notify the enduser computing device that the second portion corresponds to a contentitem object selected during a real-time content selection process (e.g.,by the content selector component 118). However, the data processingsystem 102 can have limited access to different types of interfaces toprovide the notification to the end user of the computing device 104.For example, the computing device 104 may not include a display device,or the display device may be disabled or turned off. The display deviceof the computing device 104 may consume greater resources than thespeaker of the computing device 104, so it may be less efficient to turnon the display device of the computing device 104 as compared to usingthe speaker of the computing device 104 to convey the notification.Thus, in some cases, the data processing system 102 can improve theefficiency and effectiveness of information transmission over one ormore interfaces or one or more types of computer networks. For example,the data processing system 102 (e.g., via the audio signal generatorcomponent 122) can module the portion of the output audio signalcomprising the content item to provide the indication or notificationthe end user that that portion of the output signal comprises thesponsored content item.

The data processing system 102 (e.g., via interface 110 and network 105)can transmit data packets comprising the output signal generated by theaudio signal generator component 122. The output signal can cause theaudio driver component 138 of or executed by the client device 104 todrive a speaker (e.g., transducer 136) of the client device 104 togenerate an acoustic wave corresponding to the output signal.

The data processing system 102 can include a feedback monitor component120. The feedback monitor component 120 can include hardware or softwareto measure the characteristic of the communication session. The feedbackmonitor component 120 can receive data packets carrying auditory signalstransmitted between the client device (e.g., computing device 104) and aconversational application programming interface (e.g., NLP component112 executed by the data processing system or the service provider NLPcomponent 142 executed by the service provider device 108, a third partyprovider device, or the content provider device 106) that established acommunication session with the client device responsive to interactionwith the content item. In some cases, the content provider device 106can execute an NLP component comprising one or more functions orcomponents of the service provider NLP component 142 or the NLPcomponent 112. The NLP component executed by the service provider device108 or the content provider device 106 can be customized for the serviceprovider device 108 or the content provider device 106. By customizingthe NLP component, the NLP component can reduce bandwidth usage andrequest-responses as compared to a generic or standard NLP componentbecause the NLP component can be configured with more precise queriesand responses that result in reduced back-and-forth between the NLPcomponent and the client computing device 104.

The feedback monitor component 120 can measure a characteristic of thecommunication session based on the auditory signals. The feedbackmonitor component 120 can generate a quality signal based on themeasured characteristic. The quality signal can include or refer to aquality level, quality metric, quality score or quality level. Thequality signal can include, for example, a numeric score (e.g., 0 to 10with 0 being lowest quality and 10 being highest quality, or viceversa), a letter grade (e.g., A to F with A being the best quality), abinary value (e.g., Yes/No; Good/Bad; I/O; high/low), rank, orpercentile. The quality signal can include an average quality signaldetermined from communications between a plurality of client devicesthat communicate with a same NLP component or provider device 106 or108.

The feedback monitor component 120 can measure the characteristic of thecommunication session using various measuring techniques, heuristictechniques, policies, conditions, or tests. The feedback monitorcomponent 120 can parse data packets transmitted between the clientdevice 104 and the content provider device, third party device, serviceprovider or data processing system to determine a characteristic of thecommunication session. The quality can refer to the quality of thecommunication channel used to transmit the data or the quality of thedata being communicated. For example, the quality of the communicationchannel can refer to a signal-to-noise ratio, ambient noise level,delay, lag, latency, choppiness, an echo, or dropped calls. The qualityof the data being communicated can refer to the quality of the responsesgenerated by the NLP component that is responding to audio signalsdetected by the microphone of the computing device. The quality of thedata can be based on the responsiveness of the NLP component, accuracyof the NLP component, or latency between the NLP component receiving theaudio signal or query from the client device 104 and transmitting aresponse.

The feedback monitor component 120 can determine the quality of thecommunication channel by measuring the amount of background noise andthe signal level to determine the signal-to-noise (“SNR”) ratio. Thefeedback monitor component 120 can compare the measured or determinedSNR to a threshold to determine the quality level. For example, a 10 dBSNR may be considered good. The thresholds can be predetermined ordetermined via a machine learning model (e.g., based on feedback from aplurality of devices).

The feedback monitor component 120 can further determine the quality ofthe communication channel based on the ping time between the clientdevice 104 and the provider device or data processing system. The dataprocessing system can compare the ping time with a threshold todetermine the quality level. For example, the ping threshold can be 20ms, 30 ms, 50 ms, 100 ms, 200 ms or more. The feedback monitor component120 can determine the quality of the communication channel based onchoppiness of the audio (e.g., pauses or breaks in the audio; the audiocutting out). The feedback monitor component 120 can identify an echo inthe communication channel to determine a low quality level. The feedbackmonitor component 120 can determine the number of dropped call for theNLP component during a time interval or a ratio of dropped call to totalcalls, and compare that with a threshold to determine the quality level.For example, the threshold can be 2 dropped calls per hour; or 1 droppedcall for every 100 calls.

The feedback monitor component 120 can determine the quality of thecommunication session based on the quality of the responses generated bythe NLP component (or conversational API) that is communicating with theclient computing device 104. The quality of the responses can include orbe based on, for example, the amount of time the NLP component takes togenerate a response, the text of the response, the accuracy of theresponse, the relevancy of the response, a semantic analysis of theresponse, or a network activity of the client device in response to theresponse provided by the NLP component. The feedback monitor component120 can determine the amount of time the NLP component takes to generatethe response by differencing a timestamp corresponding to when the NLPcomponent receives the audio signals from the client device 104, and atimestamp corresponding to when the NLP transmits the response. Thefeedback monitor component 120 can determine the amount of time bydifferencing a time stamp corresponding to when the client devicetransmits the audio signals and a time stamp corresponding to when theclient device receives the response from the NLP component.

The feedback monitor component 120 can determine the quality of theresponse by parsing data packets comprising the response. For example,the feedback monitor component 120 can parse and analyze the text of theresponse, the accuracy of the response, or the relevancy of the responseto the query from the client device. The feedback monitor component 120can perform this assessment by providing the query to another NLPcomponent and compare the responses from the two NLP components. Thefeedback monitor component 120 can perform this assessment by providingthe query and response to a third party assessor. The feedback monitorcomponent 120 can determine the consistency of the response by comparinga plurality of responses to a plurality of similar queries provided by aplurality of client devices. The feedback monitor component 120 candetermine the quality of the response based on the number of times theclient device transmits audio signals comprising the same query (e.g.,indicating that the responses have not been fully responsive to thequery submitted by the client device).

The feedback monitor component 120 can determine the quality of theresponse generated by the NLP based on network activity of the clientdevice. For example, the NLP component can receive a voice query fromthe client device, generate a response to the voice query, and transmitdata packets carrying the response to the client device. The clientdevice, upon receiving the response from the NLP component, can performa network activity or change a network activity. For example, the clientdevice can terminate the communication session, which can indicate thatthe NLP component was fully responsive to the client device, or the NLPfailed to successfully respond to the client device and the clientdevice gave up on the NLP component. The feedback monitor component candetermine that the client device terminated the call for good or badreasons based on a confidence score associated with the responsegenerated by the NLP component. The confidence score can be associatedwith a probabilistic or statistical semantic analysis used to generatethe response.

The feedback monitor component 120 can determine that the client deviceterminated the communication session based on an absence of audiosignals transmitted by the client device. The feedback monitor component120 can determine that the client device terminated the communicationsession based on a terminate or end command transmitted by the clientdevice. The feedback monitor component 120 can determine a quality levelbased on an amount of silence from the client device (e.g., absence ofaudio signals). The absence of audio signals can be identified based onthe SNR from the client device being less than a threshold (e.g., 6 dB,3 dB, or 0 dB). The feedback monitor component can measure thecharacteristic based on a duration of the communication session. Forexample, a duration greater than a threshold can indicate that the enduser of the client device was satisfied with the communication session.However, a long duration combined with other characteristics such as anincreased amplitude of audio signals, repeated queries, and decreasedtempo may indicate a low quality since the user of the client may havespent an unnecessary or unwanted extended amount of time engaged withthe communication session.

The NLP component can perform a semantic analysis on the queriestransmitted by the client device to determine that the client devicerepeatedly transmits the same or similar queries even though the NLPcomponent is generated and providing responses. The feedback monitorcomponent 120 can determine, based on the number of repeat querieswithin a time interval (or sequentially repeated queries) exceeding athreshold (e.g., 2, 3, 4, 5, 6, 7 or more), that the quality level islow.

In some cases, the feedback monitor component 120 can determine thequality of the communication session at different parts of thecommunication session (e.g., beginning, middle, or end; or timeintervals). The for example, the feedback monitor component 120 candetermine the quality of a first portion or first time interval of thecommunication session; and the quality of a second portion or secondtime interval in the communication session that is subsequent to thefirst portion or first time interval. The feedback monitor component 120can compare the quality at the two portions to determine a quality ofthe overall communication session. For example, a difference in qualitybetween the two portions that is greater than a threshold can indicate alow quality, inconsistent quality, or unreliable quality.

In some cases, the feedback monitor component 120 can determine thequality based on a characteristic of the communication session or atleast a portion thereof. The characteristic can include, for example, atleast one of amplitude, frequency, tempo, tone, and pitch. For example,the feedback monitor component 120 can use the characteristic todetermine a reaction of the user of the client device or sentiment ofthe use of the client. For example, if the amplitude of the audiosignals transmitted by the client device increases after each responsefrom the NLP, the feedback monitor can determine that the end user isfrustrated with the NLP component generated responses. The feedbackmonitor component 120 can compare the amplitude of the audio signalsdetected by the client device with a threshold or with other audiosignals received by the client device during the same communicationsession or different communication sessions.

The feedback monitor component 120 can determine the quality based on acharacteristic such as the tempo or pitch of the audio signals detectedby the client device and transmitted to the NLP component. The feedbackmonitor component 120 can determine, for example, that a slowing down ofthe tempo (e.g., rate of words spoken per time interval) after each NLPresponse can indicate that the end user is not satisfied with theresponse generated by the NLP component and is repeating it slower toallow the NLP component to better parse the audio signals and improvethe response. In some cases, an increase or steady tempo can indicatethat the use of the client device is satisfied with the responsesgenerated by the NLP and has confidence in the responses. In some cases,an increase in the pitch of the audio signals detected by the clientdevice can indicate a poor quality of responses from the NLP or lack ofconfidence in the responses.

In some cases, the feedback monitor component 120 can transmit queriesto the client device to measure or determine the quality. For example,the feedback monitor component 120 can transmit survey questions to theend user asking about the quality of the communication session, NLPcomponent, or provider device. In some cases, the feedback monitorcomponent 120 can generate the query responsive to the feedback monitorcomponent 120 determining that a first quality signal is below athreshold. For example, the feedback monitor component 120 can determinea first quality signal based on measuring the quality usingcharacteristics such as the increase in amplitude of the audio signalsdetected by the client device in combination with the decrease in tempoof the audio signals detected by the client device. The feedback monitorcomponent 120 can generate a quality signal indicating a low level ofquality based on the combined characteristics of amplitude and tempo.Responsive to the low quality signals determined based on thecombination characteristic, the feedback monitor component 120 cangenerate and transmit a query to the client device that eitherimplicitly or explicitly enquires about the quality of the communicationsession (e.g., How satisfied are you with the responses generated by theNLP component?; How satisfied are you with the communication session?).In another example, the data processing system can determine a qualitybased on whether the service provider 108 can provide the requestedservice. For example, the end user may request a product or service, butthe service provider 108 responds stating that they do not have thatproduct or cannot perform that service, which can cause the end user toindicate frustration with the service provider 108. The data processingsystem 102 can identify this frustration, and assign a qualityaccordingly.

In some cases, the feedback monitor component 120 can measure thecharacteristic based on network activity on multiple electronicsurfaces, and aggregate the quality measured from the multipleelectronic surfaces to generate a summed quality signal. The summedquality signal can be an average, weighted average, absolute sum, orother combined quality signal value. The feedback monitor component 120can further generate statistics for the combined quality signal value orperform a statistical analysis, such as determine the standarddeviation, variance, 3 sigma quality, or 6 sigma qualities.

The feedback monitor component 120 can adjust the real-time contentselection process performed by the content selector component 118.Adjusting the real-time content selection process can refer to adjustinga weight used to select the content item provided by the contentprovider device 106 or service provider device 108 or third partyprovider device 108 that executed the NLP component used to establishthe communication session with the client device 104. For example, ifthe content item led to a low quality communication session, thefeedback monitor component 120 can adjust an attribute or parameter ofthe content data 130 comprising the content item to reduce thelikelihood of that content item being selected for similar action datastructures or similar client devices 104 (or accounts or profilesthereof).

In some cases, the feedback monitor component 120 can prevent or blockthe content selector component 118 from selection, in the real-timeselection process, of the content item responsive to the quality signalless than a threshold. In some cases, the feedback monitor component 120can allow or permit the content selector component 118 to select, in thereal-time selection process, the content item responsive to the qualitysignal greater than or equal to a threshold.

FIG. 2 is an illustration of an operation of a feedback control systemfor data transmissions over a computer network. The system can includeone or more component of system 100 depicted in FIG. 1. The system 100can include one or more electronic surfaces 202 a-n that are executed orprovided by one or more client computing devices 104 a-n. Examples ofelectronic surfaces 202 a-n can include audio interfaces, voice-basedinterfaces, display screen, HTML content items, multimedia, images,video, text-based content items, SMS, messaging application, chatapplication, or natural language processors.

At ACT 204, the client computing device 104 can receive data packets,signals or other information indicative of a feedback from or via anelectronic surface 202. At ACT 206, the one or more client computingdevices 104 a-n, one or more service provider devices 108 a-n, or theone or more content provider devices 106 a-n can transmit data packetsto the feedback monitor component 124. The data packets can beassociated with the communication session established between the clientdevice 104 and one or more of the service provider device 108 or thecontent provider device 106. The data packets can be transmitted from arespective device to the feedback monitor component 124.

In some cases, the feedback monitor component 124 may intercept datapackets transmitted from a device 104, 106 or 108 to a respectivedevice. The feedback monitor component 124 can analyze the intercepteddata packet and route or forward the data packet to its intendeddestination. Thus, the feedback monitor component 124 can beintermediary to the client device 104 and the service/third partyprovider device 108 or the content provider device 106.

At ACT 208, the feedback monitor component 124 can transmit theintercepted or received data packets from the communication session tothe NLP component 112. At ACT 210, the NLP component 112 can perform asemantic analysis of the data packets and provide them back to thefeedback component 124. In some cases, the NLP component 112 can performnatural language processing on the audio signals from the communicationsession 206 to compare the NLP component's responses generated by theprovider devices 106 or 108. The feedback monitor component 124 cancompare the responses generated by a control NLP component 112 todetermine whether the third party NLP components are functioning on acomparable or satisfactory level.

At ACT 212, the feedback monitor component 124 can determine a qualitysignal for the communication session 206, and adjust the real-timecontent selection process performed by the content selector component118 such that the next time the content selector component 118 receivesa request for content, the content selector component 118 canappropriately weight the content item (or content provider) associatedwith the communication session 206 to either increase or decrease thelikelihood of the content item being selected. For example, if provider108 is associated with a plurality of low quality communication session,the feedback monitor component 124 can instruct the content selectorcomponent 118 to prevent selecting content items that can result inestablishment of a communication session with provider 108.

FIG. 3 is an illustration of an example method for performing dynamicmodulation of packetized audio signals. The method 300 can be performedby one or more component, system or element of system 100 or system 400.The method 300 can include a data processing system receiving an inputaudio signal (ACT 305). The data processing system can receive the inputaudio signal from a client computing device. For example, a naturallanguage processor component executed by the data processing system canreceive the input audio signal from a client computing device via aninterface of the data processing system. The data processing system canreceive data packets that carry or include the input audio signaldetected by a sensor of the client computing device (or client device).

At ACT 310, the method 300 can include the data processing systemparsing the input audio signal. The natural language processor componentcan parse the input audio signal to identify a request and a triggerkeyword corresponding to the request. For example, the audio signaldetected by the client device can include “Okay device, I need a ridefrom Taxi Service Company A to go to 1234 Main Street.” In this audiosignal, the initial trigger keyword can include “okay device”, which canindicate to the client device to transmit an input audio signal to thedata processing system. A pre-processor of the client device can filterout the terms “okay device” prior to sending the remaining audio signalto the data processing system. In some cases, the client device canfilter out additional terms or generate keywords to transmit to the dataprocessing system for further processing.

The data processing system can identify a trigger keyword in the inputaudio signal. The trigger keyword can include, for example, “to go to”or “ride” or variations of these terms. The trigger keyword can indicatea type of service or product. The data processing system can identify arequest in the input audio signal. The request can be determined basedon the terms “I need”. The trigger keyword and request can be determinedusing a semantic processing technique or other natural languageprocessing technique.

In some cases, the data processing system can generate an action datastructure. The data processing system can generate the action datastructure based on the trigger keyword, request, third party providerdevice, or other information. The action data structure can beresponsive to the request. For example, if the end user of the clientcomputing device requests a taxi from Taxi Service Company A, the actiondata structure can include information to request a taxi service fromTaxi Service Company A. The data processing system can select a templatefor Taxi Service Company A, and populate fields in the template withvalues to allow the Taxi Service Company A to send a taxi to the user ofthe client computing device to pick up the user and transport the userto the requested destination.

At ACT 315, the data processing system can select a content item. Forexample, a content selector component can receive the trigger keyword,request or action data structure and select a content item via areal-time content selection process. The selected content item cancorrespond to a content provider, service provider, or other third partyprovider. The client device can interact with the content item toestablish a communication session with the provider of the content itemor other device associated with the content item. The device associatedwith the content item can interact with the client device using aconversational API, such as an NLP.

At ACT 320, a feedback monitor component can receive data packetscarrying auditory signals transmitted between the client device and aconversational application programming interface that established acommunication session with the client device responsive to interactionwith the content item. At ACT 325, the feedback monitor component canmeasure a quality or characteristic of the communication session basedon the auditory signals, and generate a quality signal based on themeasured characteristic. At ACT 330 the feedback monitor component ordata processing system can adjust the real-time selection process basedon the quality signal.

FIG. 4 is a block diagram of an example computer system 400. Thecomputer system or computing device 400 can include or be used toimplement the system 100, or its components such as the data processingsystem 102. The data processing system 102 can include an intelligentpersonal assistant or voice-based digital assistant. The computingsystem 400 includes a bus 405 or other communication component forcommunicating information and a processor 410 or processing circuitcoupled to the bus 405 for processing information. The computing system400 can also include one or more processors 410 or processing circuitscoupled to the bus for processing information. The computing system 400also includes main memory 415, such as a random access memory (RAM) orother dynamic storage device, coupled to the bus 405 for storinginformation, and instructions to be executed by the processor 410. Themain memory 415 can be or include the data repository 145. The mainmemory 415 can also be used for storing position information, temporaryvariables, or other intermediate information during execution ofinstructions by the processor 410. The computing system 400 may furtherinclude a read only memory (ROM) 420 or other static storage devicecoupled to the bus 405 for storing static information and instructionsfor the processor 410. A storage device 425, such as a solid statedevice, magnetic disk or optical disk, can be coupled to the bus 405 topersistently store information and instructions. The storage device 425can include or be part of the data repository 145.

The computing system 400 may be coupled via the bus 405 to a display435, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 430, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 405 for communicating information and command selections to theprocessor 410. The input device 430 can include a touch screen display435. The input device 430 can also include a cursor control, such as amouse, a trackball, or cursor direction keys, for communicatingdirection information and command selections to the processor 410 andfor controlling cursor movement on the display 435. The display 435 canbe part of the data processing system 102, the client computing device150 or other component of FIG. 1, for example.

The processes, systems and methods described herein can be implementedby the computing system 400 in response to the processor 410 executingan arrangement of instructions contained in main memory 415. Suchinstructions can be read into main memory 415 from anothercomputer-readable medium, such as the storage device 425. Execution ofthe arrangement of instructions contained in main memory 415 causes thecomputing system 400 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory415. Hard-wired circuitry can be used in place of or in combination withsoftware instructions together with the systems and methods describedherein. Systems and methods described herein are not limited to anyspecific combination of hardware circuitry and software.

Although an example computing system has been described in FIG. 4, thesubject matter including the operations described in this specificationcan be implemented in other types of digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them.

For situations in which the systems discussed herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures that may collect personal information (e.g., information abouta user's social network, social actions or activities, a user'spreferences, or a user's location), or to control whether or how toreceive content from a content server or other data processing systemthat may be more relevant to the user. In addition, certain data may beanonymized in one or more ways before it is stored or used, so thatpersonally identifiable information is removed when generatingparameters. For example, a user's identity may be anonymized so that nopersonally identifiable information can be determined for the user, or auser's geographic location may be generalized where location informationis obtained (such as to a city, postal code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about him or her and usedby the content server.

The subject matter and the operations described in this specificationcan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. The subject matter described in thisspecification can be implemented as one or more computer programs, e.g.,one or more circuits of computer program instructions, encoded on one ormore computer storage media for execution by, or to control theoperation of, data processing apparatuses. Alternatively or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. A computer storage medium can be, or be includedin, a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination of one or more of them. While a computer storage medium isnot a propagated signal, a computer storage medium can be a source ordestination of computer program instructions encoded in an artificiallygenerated propagated signal. The computer storage medium can also be, orbe included in, one or more separate components or media (e.g., multipleCDs, disks, or other storage devices). The operations described in thisspecification can be implemented as operations performed by a dataprocessing apparatus on data stored on one or more computer-readablestorage devices or received from other sources.

The terms “data processing system” “computing device” “component” or“data processing apparatus” encompass various apparatuses, devices, andmachines for processing data, including by way of example a programmableprocessor, a computer, a system on a chip, or multiple ones, orcombinations of the foregoing. The apparatus can include special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). The apparatus can alsoinclude, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, a cross-platform runtime environment, avirtual machine, or a combination of one or more of them. The apparatusand execution environment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures. For example, the direct action API 116,content selector component 118, or NLP component 112 and other dataprocessing system 102 components can include or share one or more dataprocessing apparatuses, systems, computing devices, or processors.

A computer program (also known as a program, software, softwareapplication, app, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages,declarative or procedural languages, and can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program can correspond to a file in a filesystem. A computer program can be stored in a portion of a file thatholds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs (e.g., components of the data processing system 102)to perform actions by operating on input data and generating output. Theprocesses and logic flows can also be performed by, and apparatuses canalso be implemented as, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

The subject matter described herein can be implemented in a computingsystem that includes a back end component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a front end component, e.g., a client computer having agraphical user interface or a web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or a combination of one or more such back end,middleware, or front end components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system such as system 100 or system 400 can includeclients and servers. A client and server are generally remote from eachother and typically interact through a communication network (e.g., thenetwork 165). The relationship of client and server arises by virtue ofcomputer programs running on the respective computers and having aclient-server relationship to each other. In some implementations, aserver transmits data (e.g., data packets representing a content item)to a client device (e.g., for purposes of displaying data to andreceiving user input from a user interacting with the client device).Data generated at the client device (e.g., a result of the userinteraction) can be received from the client device at the server (e.g.,received by the data processing system 102 from the computing device 150or the content provider computing device 155 or the service providercomputing device 160).

While operations are depicted in the drawings in a particular order,such operations are not required to be performed in the particular ordershown or in sequential order, and all illustrated operations are notrequired to be performed. Actions described herein can be performed in adifferent order.

The separation of various system components does not require separationin all implementations, and the described program components can beincluded in a single hardware or software product. For example, the NLPcomponent 110 or the content selector component 125 can be a singlecomponent, app, or program, or a logic device having one or moreprocessing circuits, or part of one or more servers of the dataprocessing system 102.

Having now described some illustrative implementations, it is apparentthat the foregoing is illustrative and not limiting, having beenpresented by way of example. In particular, although many of theexamples presented herein involve specific combinations of method actsor system elements, those acts and those elements may be combined inother ways to accomplish the same objectives. Acts, elements andfeatures discussed in connection with one implementation are notintended to be excluded from a similar role in other implementations orimplementations.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including” “comprising” “having” “containing” “involving”“characterized by” “characterized in that” and variations thereofherein, is meant to encompass the items listed thereafter, equivalentsthereof, and additional items, as well as alternate implementationsconsisting of the items listed thereafter exclusively. In oneimplementation, the systems and methods described herein consist of one,each combination of more than one, or all of the described elements,acts, or components.

Any references to implementations or elements or acts of the systems andmethods herein referred to in the singular may also embraceimplementations including a plurality of these elements, and anyreferences in plural to any implementation or element or act herein mayalso embrace implementations including only a single element. Referencesin the singular or plural form are not intended to limit the presentlydisclosed systems or methods, their components, acts, or elements tosingle or plural configurations. References to any act or element beingbased on any information, act or element may include implementationswhere the act or element is based at least in part on any information,act, or element.

Any implementation disclosed herein may be combined with any otherimplementation or embodiment, and references to “an implementation,”“some implementations,” “one implementation” or the like are notnecessarily mutually exclusive and are intended to indicate that aparticular feature, structure, or characteristic described in connectionwith the implementation may be included in at least one implementationor embodiment. Such terms as used herein are not necessarily allreferring to the same implementation. Any implementation may be combinedwith any other implementation, inclusively or exclusively, in any mannerconsistent with the aspects and implementations disclosed herein.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms. For example, a reference to “at least one of‘A’ and ‘B’” can include only ‘A’, only ‘B’, as well as both ‘A’ and‘B’. Such references used in conjunction with “comprising” or other openterminology can include additional items.

Where technical features in the drawings, detailed description or anyclaim are followed by reference signs, the reference signs have beenincluded to increase the intelligibility of the drawings, detaileddescription, and claims. Accordingly, neither the reference signs northeir absence have any limiting effect on the scope of any claimelements.

The systems and methods described herein may be embodied in otherspecific forms without departing from the characteristics thereof. Forexample, the data processing system 102 can select a content item for asubsequent action (e.g., for the third action 215) based in part on datafrom a prior action in the sequence of actions of the thread 200, suchas data from the second action 210 indicating that the second action 210is complete or about to begin. The foregoing implementations areillustrative rather than limiting of the described systems and methods.Scope of the systems and methods described herein is thus indicated bythe appended claims, rather than the foregoing description, and changesthat come within the meaning and range of equivalency of the claims areembraced therein.

What is claimed is: 1.-20. (canceled)
 21. A system for datatransmissions over a computer network, comprising: a data processingsystem comprising one or more processors and memory to: receive, from anelectronic surface provided by a client device, data packets comprisingan input audio signal; parse the input audio signal to identify arequest and a keyword associated with the request; determine acharacteristic based on the input audio signal; generate a qualitysignal based on the characteristic; and select a content item based onthe keyword and the quality signal.
 22. The system of claim 21,comprising: the data processing system to establish a communicationsession with the client device responsive to the input audio signal. 23.The system of claim 21, comprising the data processing system to: adjusta real-time content selection process based on the quality signal; andselect the content item responsive to the request and the keyword viathe real-time content selection process adjusted based on the qualitysignal.
 24. The system of claim 21, comprising the data processingsystem to: determine a first characteristic of the input audio signal ata first time interval, and a second characteristic of a second inputaudio signal received from the client device at a second time intervalsubsequent to the first time interval; and compare the firstcharacteristic with the second characteristic.
 25. The system of claim21, comprising the data processing system to: receive, from the clientdevice, a plurality of input audio signals comprising the input audiosignal; and determine the characteristic based on a comparison of theplurality of input audio signals.
 26. The system of claim 21, whereinthe characteristic includes at least one of amplitude, frequency, tempo,tone, or pitch.
 27. The system of claim 21, wherein the characteristicindicates a sentiment of a user associated with the client device. 28.The system of claim 21, comprising the data processing system to:determine the characteristic based on at least one of amplitude,frequency, tempo, tone, or pitch; and determine, based on thecharacteristic, a sentiment of a user associated with the client device.29. The system of claim 21, comprising the data processing system to:transmit a plurality of voice-based queries to the client device; andmeasure the characteristic based on responses to the plurality ofvoice-based queries.
 30. The system of claim 21, comprising the dataprocessing system to: generate a query based on the quality signal beingless than a threshold; receive a response to the query from the clientdevice; and generate a second quality signal based on the response. 31.The system of claim 21, comprising: the data processing system tomeasure the characteristic based on a duration of a communicationsession between the client device and the data processing system, thecommunication session comprising the input audio signal.
 32. The systemof claim 21, comprising the data processing system to: measure thecharacteristic based on network activity on multiple electronicsurfaces; and aggregate quality signals measured from the multipleelectronic surfaces to generate a summed quality signal.
 33. The systemof claim 21, comprising: the data processing system to permit selectionof the content item responsive to the quality signal satisfying athreshold.
 34. A method for data transmissions over a computer network,comprising: receiving, by a data processing system comprising one ormore processors and memory, from an electronic surface provided by aclient device, data packets comprising an input audio signal; parsing,by the data processing system, the input audio signal to identify arequest and a keyword associated with the request; determining, by thedata processing system, a characteristic based on the input audiosignal; generating, by the data processing system, a quality signalbased on the characteristic; and selecting, by the data processingsystem, a content item based on the keyword and the quality signal. 35.The method of claim 34, comprising: adjusting, by the data processingsystem, a real-time content selection process based on the qualitysignal; and selecting, by the data processing system, the content itemresponsive to the request and the keyword via the real-time contentselection process adjusted based on the quality signal.
 36. The methodof claim 34, comprising: receiving, by the data processing system fromthe client device, a plurality of input audio signals comprising theinput audio signal; and determining, by the data processing system, thecharacteristic based on a comparison of the plurality of input audiosignals.
 37. The method of claim 34, comprising: determining, by thedata processing system, the characteristic based on at least one ofamplitude, frequency, tempo, tone, or pitch; and determining, by thedata processing system based on the characteristic, a sentiment of auser associated with the client device.
 38. The method of claim 34,comprising: transmitting, by the data processing system, a plurality ofvoice-based queries to the client device; and measuring, by the dataprocessing system, the characteristic based on responses to theplurality of voice-based queries.
 39. The method of claim 34,comprising: measuring, by the data processing system, the characteristicbased on a duration of a communication session between the client deviceand the data processing system, the communication session comprising theinput audio signal.
 40. The method of claim 34, comprising: permitting,by the data processing system, selection of the content item responsiveto the quality signal satisfying a threshold.